Diego Milano

MONDRIAN: Annotating and Querying Databases through Colors and Blocks

Annotations play a central role in the curation of scientific databases. Despite their importance, data formats and schemas are not designed to manage the increasing variety of annotations. Moreover, DBMS’s often lack support for storing and querying annotations. Furthermore, annotations and data are only loosely coupled. This paper introduces an annotation-oriented data model for the manipulation and querying of both data and annotations. In particular, the model allows for the specification of annotations on sets of values and for effectively querying the information on their association. We use the concept of block to represent an annotated set of values. Different colors applied to the blocks represent different annotations. We introduce a color query language for our model and prove it to be both complete (it can express all possible queries over the class of annotated databases), and minimal (all the algebra operators are primitive). We present MONDRIAN, a prototype implementation of our annotation mechanism, and we conduct experiments that investigate the set of parameters which influence the evaluation cost for color queries.

Using ontologies for XML data cleaning

Real data is often affected by errors and inconsistencies. Many of them depend on the fact that schemas cannot represent a sufficiently wide range of constraints. Data cleaning is the process of identifying and possibly correcting data quality problems that affect the data. Cleaning data requires to gather knowledge on the domain to which the data refer. Anyway, existing data cleaning techniques still access this knowledge as a fragmented collection of heterogenous rules and ad hoc data transformations. Furthermore, data cleaning methodologies for an important class of data based on the semistructured XML data model have not yet been proposed. In this paper we introduce the OXC framework, that offers a methodology for XML data cleaning based on a uniform representation of domain knowledge through an ontology We describe how to define XML related data quality metrics based on our domain knowledge representation, and give a definition of various metrics related to the completeness data quality dimension.

i MONDRIAN: a visual tool to annotate and query scientific databases

We demonstrate iMONDRIAN, a component of the MONDRIAN annotation management system. Distinguishing features of MONDRIAN are (i) the ability to annotate sets of values (ii) the annotation-aware query algebra. On top of that, iMONDRIAN offers an intuitive visual interface to annotate and query scientific databases. In this demonstration, we consider Gene Ontology (GO), a publicly available biological database. Using this database we show (i) the creation of annotations through the visual interface (ii) the ability to visually build complex, annotation-aware, queries (iii) the basic functionality for tracking annotation provenance. Our demonstration also provides a cheat window which shows the system internals and how visual queries are translated to annotation-aware algebra queries.

Measuring and Diffusing Data Quality in a Peer-to-Peer Architecture

Data quality is becoming an increasingly important issue in environments characterized by extensive data replication. Among such environments, this article focuses on cooperative information systems (CISs), for which it is very important to declare and access quality of data. The article describes a general methodology for evaluating quality of data, and the design of an architectural component, named quality factory, that implements quality evaluation of XML data. The detailed design and implementation of a further service, named data quality broker, are presented. The data quality broker accesses data and related quality distributed in the CIS and improves quality of data by comparing different copies present in the system. The data quality broker has been implemented as a peer-to-peer service and a set of experiments on real data show its effectiveness and performance behavior.

Design and Implementation of a Peer-to-Peer Data Quality Broker

Data quality is becoming an increasingly important issue in environments characterized by extensive data replication. Among such environments, this paper focuses on Cooperative Information Systems (CISs), for which it is very important to declare and access quality of data. Specifically, we describe the detailed design and implementation of a peer-to-peer service for exchanging and improving data quality in CISs. Such a service allows to access data and related quality distributed in the CIS and improves quality of data by comparing different copies of the same data. Some experiments on real data will show the effectiveness of the service and the performance behavior.

A peer-to-peer service supporting data quality: Design and implementation issues

Recent research has highlighted the importance of data quality issues in environments characterized by extensive data replication, such as Cooperative Information Systems (CISs). While high data quality is a strict requirement for CISs, the high degree of data replication that characterizes such systems can be exploited to improve the quality of data, as different copies of the same data may be compared in order to detect quality problems and possibly solve them.